Internet abuse in the workplace: Issues and concerns for employers and employment counselors

This article gives an overview of some of the main issues in the most serious form of Internet abuse (i.e., Internet addiction) before examining other types of workplace Internet abuse (cybersexual Internet abuse, online friendship/relationship abuse, Internet activity abuse, online information abuse, criminal Internet abuse, and miscellaneous Internet abuse) and why they occur. A few specific types of Internet abuse (online pornography use, sexually related Internet crime, online gambling) are also highlighted as issues for employers. The author concludes that Internet abuse should be taken seriously by all employers who use the Internet in their day‐to‐day business.     

Impaired prospective memory but intact episodic memory in intellectually average 7- to 9-year-olds born very preterm and/or very low birth weight

Relatively little is known about episodic memory (EM: memory for personally-experienced events) and prospective memory (PM: memory for intended actions) in children born very preterm (VP) or with very low birth weight (VLBW). This study evaluates EM and PM in mainstream-schooled 7- to 9-year-olds born VP (≤ 32 weeks) and/or VLBW (< 1500 g) and matches full-term children for comparison (n = 35 and n = 37, respectively). Additionally, participants were assessed for verbal and non-verbal ability, executive function (EF), and theory of mind (ToM). The results show that the VP/VLBW children were outperformed by the full-term children on the memory tests overall, with a significant univariate group difference in PM. Moreover, within the VP/VLBW group, the measures of PM, verbal ability and working memory all displayed reliable negative correlations with severity of neonatal illness. PM was found to be independent of EM and cognitive functioning, suggesting that this form of memory might constitute a domain of specific vulnerability for VP/VLBW children.     

Finding the traces of behavioral and cognitive processes in big data and naturally occurring datasets

Today, people generate and store more data than ever before as they interact with both real and virtual environments. These digital traces of behavior and cognition offer cognitive scientists and psychologists an unprecedented opportunity to test theories outside the laboratory. Despite general excitement about big data and naturally occurring datasets among researchers, three “gaps” stand in the way of their wider adoption in theory-driven research: the imagination gap, the skills gap, and the culture gap. We outline an approach to bridging these three gaps while respecting our responsibilities to the public as participants in and consumers of the resulting research. To that end, we introduce Data on the Mind (http://www.dataonthemind.org), a community-focused initiative aimed at meeting the unprecedented challenges and opportunities of theory-driven research with big data and naturally occurring datasets. We argue that big data and naturally occurring datasets are most powerfully used to supplement—not supplant—traditional experimental paradigms in order to understand human behavior and cognition, and we highlight emerging ethical issues related to the collection, sharing, and use of these powerful datasets.     

A psychometric comparison of the Internet Addiction Test, the Internet-Related Problem Scale, and self-diagnosis

One of the more prominent issues in the field of Internet addiction is the validity of the instrument used to assess users' level of Internet involvement. Many of the instruments used to assess Internet addiction have high face validity but have yet to be tested psychometrically. The aim of this study is to compare two of the most used Internet addiction research measures, the Internet Addiction Test (IAT) and the Internet-Related Problem Scale (IRPS), along with a self-diagnostic question simply asking Internet users if they thought they were addicted to the Internet. A total of 225 Internet users participated in the study (69 males and 156 females). Participants who defined themselves as Internet addicts had higher scores on both the IAT and IRPS, and the three different Internet addiction measures were strongly correlated to each other. For the IAT, factor analysis generated three factors (emotional/psychological conflict; time management issues; mood modification) explaining 56.3% of the variance. For the IRPS, factor analysis generated four factors (negative effects of Internet use; mood modification; loss of control; increased Internet use) explaining 60.2% of the variance. The implications for these findings are discussed.     

A rational model of the effects of distributional information on feature learning

Most psychological theories treat the features of objects as being fixed and immediately available to observers. However, novel objects have an infinite array of properties that could potentially be encoded as features, raising the question of how people learn which features to use in representing those objects. We focus on the effects of distributional information on feature learning, considering how a rational agent should use statistical information about the properties of objects in identifying features. Inspired by previous behavioral results on human feature learning, we present an ideal observer model based on nonparametric Bayesian statistics. This model balances the idea that objects have potentially infinitely many features with the goal of using a relatively small number of features to represent any finite set of objects. We then explore the predictions of this ideal observer model. In particular, we investigate whether people are sensitive to how parts co-vary over objects they observe. In a series of four behavioral experiments (three using visual stimuli, one using conceptual stimuli), we demonstrate that people infer different features to represent the same four objects depending on the distribution of parts over the objects they observe. Additionally in all four experiments, the features people infer have consequences for how they generalize properties to novel objects. We also show that simple models that use the raw sensory data as inputs and standard dimensionality reduction techniques (principal component analysis and independent component analysis) are insufficient to explain our results.     

Bayes and blickets: effects of knowledge on causal induction in children and adults

People are adept at inferring novel causal relations, even from only a few observations. Prior knowledge about the probability of encountering causal relations of various types and the nature of the mechanisms relating causes and effects plays a crucial role in these inferences. We test a formal account of how this knowledge can be used and acquired, based on analyzing causal induction as Bayesian inference. Five studies explored the predictions of this account with adults and 4-year-olds, using tasks in which participants learned about the causal properties of a set of objects. The studies varied the two factors that our Bayesian approach predicted should be relevant to causal induction: the prior probability with which causal relations exist, and the assumption of a deterministic or a probabilistic relation between cause and effect. Adults' judgments (Experiments 1, 2, and 4) were in close correspondence with the quantitative predictions of the model, and children's judgments (Experiments 3 and 5) agreed qualitatively with this account.     

Overt attention and predictiveness in human contingency learning

Two experiments used eye-tracking procedures to investigate the relationship between attention and associative learning in human participants. These experiments found greater overt attention to cues experienced as predictive of the outcomes with which they were paired, than to cues experienced as nonpredictive. Moreover, this attentional bias persisted into a second training phase when all cues were equally predictive of the outcomes with which they were paired, and it was accompanied by a related bias in the rate of learning about these cues. These findings are consistent with the attentional model of associative learning proposed by Mackintosh (1975), but not with that proposed by Pearce and Hall (1980).     

Superstition predicts perception of illusory control

Superstitions are common, yet we have little understanding of the cognitive mechanisms that bring them about. This study used a laboratory‐based analogue for superstitious beliefs that involved people monitoring the relationship between undertaking an action (pressing a button) and an outcome occurring (a light illuminating). The task was arranged such that there was no objective contingency between pressing the button and the light illuminating – the light was just as likely to illuminate whether the button was pressed or not. Nevertheless, most people rated the causal relationship between the button press and the light illuminating to be moderately positive, demonstrating an illusion of causality. This study found that the magnitude of this illusion was predicted by people's level of endorsement of common superstitious beliefs (measured using a novel Superstitious Beliefs Questionnaire), but was not associated with mood variables or their self‐rated locus of control. This observation is consistent with a more general individual difference or bias to overweight conjunctive events over disjunctive events during causal reasoning in those with a propensity for superstitious beliefs.